Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Sharing of the frequency bands between radar and communication systems has attracted substantial attention, as it can avoid under-utilization of otherwise permanently allocated spectral resources, thus improving efficiency. Further, there is increasing demand for radar and communication systems that share the hardware platform as well as the frequency band, as this not only decongests the spectrum, but also benefits both sensing and signaling operations via the full cooperation between both functionalities. Nevertheless, the success of spectrum and hardware sharing between radar and communication systems critically depends on high-quality joint radar and communication designs. In the first part of this paper, we overview the research progress in the areas of radar-communication coexistence and dual-functional radar-communication (DFRC) systems, with particular emphasis on application scenarios and technical approaches. In the second part, we propose a novel transceiver architecture and frame structure for a DFRC base station (BS) operating in the millimeter wave (mmWave) band, using the hybrid analog-digital (HAD) beamforming technique. We assume that the BS is serving a multi-antenna user equipment (UE) over a mmWave channel, and at the same time it actively detects targets. The targets also play the role of scatterers for the communication signal. In that framework, we propose a novel scheme for joint target search and communication channel estimation, which relies on omni-directional pilot signals generated by the HAD structure. Given a fully-digital communication precoder and a desired radar transmit beampattern, we propose to design the analog and digital precoders under non-convex constant-modulus (CM) and power constraints, such that the BS can formulate narrow beams towards all the targets, while pre-equalizing the impact of the communication channel. Furthermore, we design a HAD receiver that can simultaneously process signals from the UE and echo waves from the targets. By tracking the angular variation of the targets, we show that it is possible to recover the target echoes and mitigate the resulting interference to the UE signals, even when the radar and communication signals share the same signal-to-noise ratio (SNR). The feasibility and efficiency of the proposed approaches in realizing DFRC are verified via numerical simulations. Finally, the paper concludes with an overview of the open problems in the research field of communication and radar spectrum sharing (CRSS).

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Joint Radar and Communication Design: Applications, State-of-the-Art, and the Road Ahead

Joint radar and communication (JRC) has recently attracted substantial attention. The first reason is that JRC allows individual radar and communication systems to share spectrum bands and thus improves the spectrum utilization. The second reason is that JRC enables a single hardware platform, e.g., an autonomous vehicle or a UAV, to simultaneously perform the communication function and the radar function. As a result, JRC is able to improve the efficiency of resources, i.e., spectrum and energy, reduce the system size, and minimize the system cost. However, there are several challenges to be solved for the JRC design. In particular, sharing the spectrum imposes the interference caused by the systems, and sharing the hardware platform and energy resource complicates the design of the JRC transmitter and compromises the performance of each function. To address the challenges, several resource management approaches have been recently proposed, and this paper presents a comprehensive literature review on resource management for JRC. First, we give fundamental concepts of JRC, important performance metrics used in JRC systems, and applications of the JRC systems. Then, we review and analyze resource management approaches, i.e., spectrum sharing, power allocation, and interference management, for JRC. In addition, we present security issues to JRC and provide a discussion of countermeasures to the security issues. Finally, we highlight important challenges in the JRC design and discuss future research directions related to JRC.

Radio Resource Management in Joint Radar and Communication: A Comprehensive Survey

This paper investigates the problem of power minimization-based joint subcarrier assignment and power allocation (PM-JSAPA) for an integrated radar and communications system (IRCS). In the considered system, the integrated transmitter is responsible to fulfill both radar and communications purposes simultaneously. The proposed PM-JSAPA strategy aims to minimize the total radiated power of the IRCS by jointly optimizing the available subcarriers and power resource allocation in order to achieve improved power-saving performance, subject to a predetermined mutual information (MI) for target parameter estimation and a specified data information rate (DIR) for wireless communications. The formulated problem falls into a mixed integer nonlinear programming (MINLP) problem, which is generally NP-hard. Then, an efficient three-step resource allocation framework is designed to solve the resulting optimization problem. Specifically, in Step 1, the subcarrier assignment for both radar and communications purposes is determined. In Step 2 and Step 3, we perform power allocation for different applications with the obtained subcarrier assignment results in Step 1, respectively. Finally, numerical simulation results are presented to demonstrate the superior power-saving performance of the proposed PM-JSAPA strategy and the effectiveness of the proposed solutions.

Joint Subcarrier Assignment and Power Allocation Strategy for Integrated Radar and Communications System Based on Power Minimization

This article studies the physical layer security in a multiple-input-multiple-output (MIMO) dual-functional radar-communication (DFRC) system, which communicates with downlink cellular users and tracks radar targets simultaneously. Here, the radar targets are considered as potential eavesdroppers which might eavesdrop the information from the communication transmitter to legitimate users. To ensure the transmission secrecy, we employ artificial noise (AN) at the transmitter and formulate optimization problems by minimizing the signal-to-noise ratio (SNR) received at radar targets, while guaranteeing the signal-to-interference-plus-noise ratio (SINR) requirement at legitimate users. We first consider the ideal case where both the target angle and the channel state information (CSI) are precisely known. The scenario is further extended to more general cases with target location uncertainty and CSI errors, where we propose robust optimization approaches to guarantee the worst-case performance. Accordingly, the computational complexity is analyzed for each proposed method. Our numerical results show the feasibility of the algorithms with the existence of instantaneous and statistical CSI error. In addition, the secrecy rate of secure DFRC system grows with the increasing angular interval of location uncertainty.

Secure Radar-Communication Systems With Malicious Targets: Integrating Radar, Communications and Jamming Functionalities

In this paper, we investigate transmit beampattern optimization techniques for a multiple-input multiple-output radar in the presence of a legitimate communications receiver and an eavesdropping target. The primary objectives of the radar are to satisfy a certain target-detection criterion and to simultaneously communicate safely with a legitimate receiver by maximizing the secrecy rate against the eavesdropping target. Therefore, we consider three optimization problems, namely target return signal-to-interference-plus-noise ratio maximization, secrecy rate maximization, and transmit power minimization. However, these problems are nonconvex due to the nonconcavity of the secrecy rate function, which appears in all three optimizations either as the objective function or as a constraint. To solve this issue, we use Taylor series approximation of the nonconvex elements through an iterative algorithm, which recasts the problem as a convex problem. Two transmit covariance matrices are designed to detect the target and convey the information safely to the communication receiver. Simulation results are presented to validate the efficiency of the aforementioned optimizations.

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Secrecy Rate Optimizations for MIMO Communication Radar

In this paper, we propose a technique for the joint tracking and labeling of multiple extended targets. To achieve multiple extended target tracking using this technique, models for the target measurement rate, kinematic component, and target extension are defined and jointly propagated in time under the generalized labeled multi-Bernoulli filter framework. In particular, we developed a Poisson mixture variational Bayesian model to simultaneously estimate the measurement rate of multiple extended targets and extended target extension was modeled using B-splines. We evaluated our proposed method with various performance metrics. Results demonstrate the effectiveness of our approach.

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Bayesian Multiple Extended Target Tracking Using Labeled Random Finite Sets and Splines

We propose an efficient sequential Monte Carlo probability hypothesis density (PHD) filter which employs the Kalman-gain approach during weight update to correct predicted particle states by minimizing the mean square error between the estimated measurement and the actual measurement received at a given time in order to arrive at a more accurate posterior. This technique identifies and selects those particles belonging to a particular target from a given PHD for state correction during weight computation. Besides the improved tracking accuracy, fewer particles are required in the proposed approach. Simulation results confirm the improved tracking performance when evaluated with different measures.

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Kalman-Gain Aided Particle PHD Filter for Multitarget Tracking

The electronic intelligence (ELINT) system is used by the military to detect, extract information and classify incoming radar signals. This work utilizes short time Fourier transform (STFT) - time frequency distribution (TFD) for inter-pulse analysis of the radar signal in order to estimate basic radar signal time parameters (pulse width and pulse repetition period). Four well-known windows functions of different and unique characteristics were used for the localization of STFT to determine their various effects on the analysis. The window functions are Hamming, Hanning, Bartlett and Blackman window functions. Monte Carlo simulation is carried out to determine the performance of the signal analysis in presence of additive white Gaussian noise (AWGN). Results show that the lower the transition of main lobe width and higher the peak side lobe, the better the performance of the window function irrespective of time parameter being estimated. This is because 100 percent probability of correct estimation is achieved at signal to noise ratio of about -2dB for Bartlett, 4dB for both Hamming and Hanning, and 9dB for Blackman.

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Selection of window for inter-pulse analysis of simple pulsed radar signal using the short time Fourier transform

Resampling is an essential step in particle filtering (PF) methods in order to avoid degeneracy. Systematic resampling is one of a number of resampling techniques commonly used due to some of its desirable properties such as ease of implementation and low computational complexity. However, it has a tendency of resampling very low weight particles especially when a large number of resampled particles are required which may affect state estimation. In this paper, we propose an improved version of the systematic resampling technique which addresses this problem and demonstrate performance improvement.

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Abdullahi Daniyan

Papers

Secrecy Rate Optimizations for MIMO Communication Radar

Bayesian Multiple Extended Target Tracking Using Labeled Random Finite Sets and Splines

Kalman-Gain Aided Particle PHD Filter for Multitarget Tracking

Selection of window for inter-pulse analysis of simple pulsed radar signal using the short time Fourier transform

An improved resampling approach for particle filters in tracking